Outline

  1. About me
  2. Adaptation in stochastic environments
  3. Some of my ML projects
    • Campanula range expansion (WSU)
    • Fine-scale zoonotic reservoirs (UIdaho)
    • Surveillance sensitivity (EHA)
  4. Closing
  5. Questions

About Me

Adaptation in Stochastic Environments

  • Bet-Hedging
    • Definition: Reducing variance in fitness at the expense of lower average fitness to ensure survival across unpredictable conditions.
    • Examples: Plants producing seeds with staggered germination times.
  • Life History Adjustments
    • r-selection: High fecundity with low parental investment (e.g., many offspring).
    • K-selection: Fewer offspring with higher investment (e.g., longevity, parental care).
    • Dormancy: Delayed development (e.g., seed banks).
  • Boosting Genetic Diversity
    • Role: Buffers populations against fluctuations, ensuring some individuals thrive in any condition.
    • Mechanisms: Variation of mutation rate, sexual reproduction, dispersal / gene flow.
  • Phenotypic Plasticity
    • Definition: Non-parallel reaction norms among individuals with different genotypes in response to different environmental conditions. GxE interaction.
    • Examples: Seasonal coat color changes in animals; metabolic flexibility in plants.

These Hypotheses can Be Tested

“The fitness of a lineage in a fluctuating environment is the time average of its fitness over the sequence of static conditions it encounters.”1

Different Kinds of Mean

 

Machine Learning

XKCD’s definition

 

Large Language Models

Modeling Language

 

1.
Abreu, C. I., Mathur, S. & Petrov, D. A. Environmental memory alters the fitness effects of adaptive mutations in fluctuating environments. Nat. Ecol. Evol. 8, 1760–1775 (2024).